Relaxometry Guided Quantitative Cardiac Magnetic Resonance Image Reconstruction

arXiv:2403.00549v1 Announce Type: cross
Abstract: Deep learning-based methods have achieved prestigious performance for magnetic resonance imaging (MRI) reconstruction, enabling fast imaging for many clinical applications. Previous methods employ convolutional networks to learn the image prior as the regularization term. In quantitative MRI, the physical model of nuclear magnetic resonance relaxometry is known, providing additional prior knowledge for image reconstruction. However, traditional reconstruction networks are limited to learning the spatial domain prior knowledge, ignoring the relaxometry prior. Therefore, we propose a …

abstract applications arxiv clinical cs.cv deep learning eess.iv enabling image imaging learn mri networks nuclear performance prior quantitative regularization type